Estimation in Generalized Mixed Models

SUMMARY Regression models containing fixed and random effects may have a response variable which is not normally distributed. The generalized mixed model includes both discrete and continuous response variables and is developed here for problems in which the regression variables enter linearly into the model. Best linear unbiased predictor methods are extended to maximum likelihood and residual maximum likelihood estimation procedures. Applications in modelling discrete response variables and in survival analysis are discussed.